Abstract
In the paper, ULS, a conjunctive clustering system working on structured descriptions, is described. It uses background knowledge defined as a theory about the classification goals. It involves learning category structure from previous information concerning the features and the domain in order to simplify, at least from a computational point of view, the clustering process. ULS explicitly couples clustering and characterization: the task of clustering is performed adopting a similarity based approach while characterization aims at intensionally defining concepts. The background knowledge is adopted in clustering, in order to partition the initial space by the definition of class prototypes, and in characterization, in order to define some heuristics useful to guide the search process in concept generalization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
BRITO, P., DIDAY, E. (1990): Piramidal representation of symbolic objects. In: M. Schader, W. Gaul (eds.): Knowledge, Data and Computer Assisted Decisions Springer Verlag, 3–16.
DIDAY, E., SIMON, J.C. (1976): Clustering Analysis. In: K.S. Fu (ed.) Digital Pattern Recognition Springer Verlag, 47–94.
ESPOSITO, F. (1990): Automatic Acquisition of Production Rules by Empirical Supervised Learning Methods. In: M. Schader, W. Gaul (eds.): Knowledge, Data and Computer Assisted Decisions Springer Verlag, 35–48.
ESPOSITO, F., MALERBA, D., SEMERARO, G. (1991a): Classification of Incomplete Structural Descriptions using a Probabilistic Distance Measure. In: E. Diday and Y. Lechevallier (eds.): Symbolic-Numeric Data Analysis and Learning Nova Science Pub., New York, 469–482.
ESPOSITO, F., MALERBA, D., SEMERARO, G. (1991b): Flexible Matching for noisy structural descriptions. In Proc. of 12th Int. Conf. on Artificial Intelligence. Sidney, Australia, Morgan Kaufmann, 658–664.
ESPOSITO, F., MALERBA, D., SEMERARO, G. (1992): Classification in noisy environments using a Distance Measure between structural symbolic descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.14, No.3, 390–402.
FISHER, D.H., (1987): Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139–172.
FISHER, D.H., PAZZANI, M. (1991): Theory Guided Concept Formation. In: D.H. Fisher, M. Pazzani and P. Langley (eds.): Concept Formation: knowledge and experience in unsupervised learning Morgan Kaufmann, San Mateo, CA.
HANSON, S.J. (1990): Conceptual Clustering and categorization. In: Y. Kodratoff and R. Michalski (eds.): Machine Learning: an Artificial Intelligence Approach, Vol. III. Morgan Kaufmann, San Mateo, CA, 235–268.
LARSON, J. (1977): Inductive Inference in the Variable Valued Predicate Logic System VL21. Ph.D. Thesis: Dept. of Computer Science, Urbana, IL.
MICHALSKI, R., DIDAY, E., STEPP, R.(1982): A recent advance in Data Analysis: clustering objects into classes characterized by conjunctive concepts. In: L. Kanal and A. Rosenfeld (eds.): Progress in Pattern Recognition, Vol. 1.
MICHALSKI, R.S. (1983): A theory and methodology of inductive learning. In: R.S. Michalski, J.G. Carbonell and T. Mitchell (eds.): Machine Learning, an Artificial Intelligence Approach Tioga, Palo Alto, CA, 83–134.
MICHALSKI, R., STEPP, R. (1983): Learning from observation: conceptual clustering. In: R. Michalski, J.C. Carbonell, T. Mitchell eds.: Machine Learning: An Artificial Intelligence Approach Tioga, Palo Alto, CA, 331–363.
STEPP, R., MICHALSKI, R., (1986): Conceptual Clustering of structured objects: a goal-oriented approach. Artificial Intelligence 28, 43–69, Elsevier Science Pub, North-Holland.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Esposito, F. (1994). Conceptual Clustering in Structured Domains: A Theory Guided Approach. In: Diday, E., Lechevallier, Y., Schader, M., Bertrand, P., Burtschy, B. (eds) New Approaches in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-51175-2_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-51175-2_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58425-4
Online ISBN: 978-3-642-51175-2
eBook Packages: Springer Book Archive